Emotional Dialogue Generation with Generative Adversarial Networks
Yun Li, Bin Wu · 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2020
Dialogue generation is getting more and more attention in the area of natural language generation, while existing dialogue models focus on the fluency and content-related for reply primarily and ignore its emotional factor. In recent years, Generative Adversarial Networks (GAN) has shown satisfactory results in text generation instead of dialogue generation. In this paper, we propose a dialogue generation framework - EDGAN, which has multiple generators and one multi-class discriminator. Multiple generators are trained to generate response with different emotion labels respectively. The human-generated dialogues and the machine-generated ones are fed to the discriminator. The output about emotion types from the discriminator are used as penalty for each generator to focus on generating its own examples of a specific emotion type accurately. The experimental results on automatic evaluation and manual evaluation demonstrate that our model can generate more high-quality emotional responses than previous baselines.